Meta-path based multi-network collective link prediction

Abstract

Online networks offering various services have become ubiquitous in our daily life. Meanwhile, users nowadays are usually involved in multiple online networks simultaneously to enjoy specific services provided by different networks. Formally, networks that share some common users are named as partially aligned networks. In this paper, we want to predict the formation of links in multiple partially aligned networks at the same time, which is formally defined as the multi-network link (formation) prediction problem. In multiple partially aligned networks, users can be extensively correlated with each other by various connections. To categorize these diverse connections among users, 7 intra-network meta paths and 4 categories of inter-network meta paths are proposed in this paper. These social meta paths can cover a wide variety of connection information in the network, some of which can be helpful for solving the multi-network link prediction problem but some can be not. To utilize useful connection, a subset of the most informative social meta paths are picked, the process of which is formally defined as social meta path selection in this paper. An effective general link formation prediction framework, Mli (Multi-network Link Identifier), is proposed in this paper to solve the multi-network link (formation) prediction problem. Built with heterogenous topological features extracted based on the selected social meta paths in the multiple partially aligned networks, Mli can help refine and disambiguate the prediction results reciprocally in all aligned networks. Extensive experiments conducted on real-world partially aligned heterogeneous networks, Foursquare and Twitter, demonstrate that Mli can solve the multi-network link prediction problem very well.